{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import and setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- import\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- figure size\n",
    "plt.rcParams['figure.figsize'] =[8, 6]\n",
    "\n",
    "# ---------- axes\n",
    "plt.rcParams['axes.grid'] = True\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "\n",
    "# ---------- ticks\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "plt.rcParams['xtick.major.width'] = 1.0\n",
    "plt.rcParams['ytick.major.width'] = 1.0\n",
    "plt.rcParams['xtick.major.size'] = 8.0\n",
    "plt.rcParams['ytick.major.size'] = 8.0\n",
    "\n",
    "# ---------- lines\n",
    "plt.rcParams['lines.linewidth'] = 2.5\n",
    "\n",
    "# ---------- grid\n",
    "plt.rcParams['grid.linestyle'] = ':'\n",
    "\n",
    "# ---------- font\n",
    "plt.rcParams['font.size'] = 20\n",
    "#plt.rcParams['pdf.fonttype'] = 42    # embed fonts in PDF using type42 (True type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pkl(filename):\n",
    "    with open(filename, 'rb') as f:\n",
    "        return pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Select</th>\n",
       "      <th>Spg_num</th>\n",
       "      <th>Spg_sym</th>\n",
       "      <th>Spg_num_opt</th>\n",
       "      <th>Spg_sym_opt</th>\n",
       "      <th>E_eV_atom</th>\n",
       "      <th>Magmom</th>\n",
       "      <th>Opt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>P-3</td>\n",
       "      <td>164</td>\n",
       "      <td>P-3m1</td>\n",
       "      <td>-4.057053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>129</td>\n",
       "      <td>P4/nmm</td>\n",
       "      <td>229</td>\n",
       "      <td>Im-3m</td>\n",
       "      <td>-4.055381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>132</td>\n",
       "      <td>P4_2/mcm</td>\n",
       "      <td>123</td>\n",
       "      <td>P4/mmm</td>\n",
       "      <td>-3.983964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>133</td>\n",
       "      <td>P4_2/nbc</td>\n",
       "      <td>132</td>\n",
       "      <td>P4_2/mcm</td>\n",
       "      <td>-3.877170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>P3m1</td>\n",
       "      <td>156</td>\n",
       "      <td>P3m1</td>\n",
       "      <td>-3.816091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>P4</td>\n",
       "      <td>125</td>\n",
       "      <td>P4/nbm</td>\n",
       "      <td>-3.785552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>129</td>\n",
       "      <td>P4/nmm</td>\n",
       "      <td>123</td>\n",
       "      <td>P4/mmm</td>\n",
       "      <td>-3.652067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>102</td>\n",
       "      <td>P4_2nm</td>\n",
       "      <td>102</td>\n",
       "      <td>P4_2nm</td>\n",
       "      <td>-3.409063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>115</td>\n",
       "      <td>P-4m2</td>\n",
       "      <td>115</td>\n",
       "      <td>P-4m2</td>\n",
       "      <td>-3.071566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>229</td>\n",
       "      <td>Im-3m</td>\n",
       "      <td>221</td>\n",
       "      <td>Pm-3m</td>\n",
       "      <td>-2.250674</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Select  Spg_num   Spg_sym  Spg_num_opt Spg_sym_opt  E_eV_atom  Magmom   Opt\n",
       "6       1      147       P-3          164       P-3m1  -4.057053     NaN  done\n",
       "2       3      129    P4/nmm          229       Im-3m  -4.055381     NaN  done\n",
       "8       3      132  P4_2/mcm          123      P4/mmm  -3.983964     NaN  done\n",
       "0       5      133  P4_2/nbc          132    P4_2/mcm  -3.877170     NaN  done\n",
       "5       1      156      P3m1          156        P3m1  -3.816091     NaN  done\n",
       "9       2       75        P4          125      P4/nbm  -3.785552     NaN  done\n",
       "3       5      129    P4/nmm          123      P4/mmm  -3.652067     NaN  done\n",
       "4       4      102    P4_2nm          102      P4_2nm  -3.409063     NaN  done\n",
       "7       4      115     P-4m2          115       P-4m2  -3.071566     NaN  done\n",
       "1       2      229     Im-3m          221       Pm-3m  -2.250674     NaN  done"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rslt_data = load_pkl('./pkl_data/rslt_data.pkl')\n",
    "\n",
    "# ---------- sort Selection\n",
    "#rslt_data.head(10)\n",
    "\n",
    "# ---------- sort by Energy\n",
    "rslt_data.sort_values(by=['E_eV_atom']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of selection: 5\n",
      "Number of data: 10\n",
      "Success: 10\n",
      "Error: 0\n",
      "Emin: -4.057052953925473 eV/atom\n"
     ]
    }
   ],
   "source": [
    "# ---------- Selection\n",
    "smax = rslt_data['Select'].max()\n",
    "print('Number of selection: {}'.format(smax))\n",
    "\n",
    "# ---------- Number of structures\n",
    "ndata = len(rslt_data)\n",
    "print('Number of data: {}'.format(ndata))\n",
    "\n",
    "# ---------- check success and error\n",
    "nsuccess = rslt_data['E_eV_atom'].count()\n",
    "nerror = ndata - nsuccess\n",
    "print('Success: {}'.format(nsuccess))\n",
    "print('Error: {}'.format(nerror))\n",
    "\n",
    "# ---------- minimum\n",
    "Emin = rslt_data['E_eV_atom'].min()\n",
    "print('Emin: {} eV/atom'.format(Emin))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Energy vs. trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Energy (eV/atom)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# ---------- axis\n",
    "dx = 1    # margin in xtick\n",
    "ax.set_xlim([1-dx, ndata+dx])\n",
    "ax.set_ylim([-0.5, 3])\n",
    "\n",
    "# ---------- hline at zero\n",
    "ax.hlines(0.0, -dx, ndata+dx, 'k', '--')\n",
    "\n",
    "# ---------- plot\n",
    "#ax.plot(rslt_data['E_eV_atom'] - Emin, 'o', ms=15, mew=2.0, alpha=0.8)\n",
    "\n",
    "\n",
    "# ---------- color coded by selection  \n",
    "tx = 0\n",
    "for s in range(1, smax+1):    # selection starts from 1\n",
    "    sfilter = rslt_data['Select'] == s\n",
    "    num = len(rslt_data[sfilter])\n",
    "    x = np.arange(1, num+1) + tx\n",
    "    ax.plot(x, rslt_data['E_eV_atom'][sfilter] - Emin, 'o', ms=15, mew=2.0, alpha=0.8)\n",
    "    tx += num\n",
    "\n",
    "# ---------- title and label\n",
    "ax.set_title('Bayesian optimization for Si$_{16}$')\n",
    "ax.set_xlabel('Number of trials')\n",
    "ax.set_ylabel('Energy (eV/atom)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- save figure\n",
    "fig.savefig('Si16_BO.png', bbox_inches='tight')    # PNG\n",
    "#fig.savefig('title_RS.png', bbox_inches='tight', dpi=300)    # high dpi PNG\n",
    "#fig.savefig('title_RS.pdf', bbox_inches='tight')    # PDF"
   ]
  }
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